Practicum
Jiaqing Ge
2/19/2020
1. Packages needed
library(dplyr)
library(ggplot2)
library(knitr)
library(kableExtra)
library(wordcloud)
library(plotly)
library(ggthemes)
library(gapminder)
library(scales)
library(usmap)
library(tidycensus)
library(lubridate)
library(TTR)
library(tm)
library(SnowballC)
library(RColorBrewer)
library(tidytext)
library(stringr)2. Import data
complaints <- read.csv('complaints.csv',stringsAsFactors = FALSE)
saveRDS(complaints, file = "complaints_raw.rds") # This line change complaints.csv to dataset that works with R.
complaints_raw <- readRDS("complaints_raw.rds")3. Randomly draw 5000 rows of data to do the analysis.
subset <- complaints_raw[sample(nrow(complaints_raw), size = 5000, replace = FALSE),]
saveRDS(subset, file = "complaints_sub.rds")4. Randomly draw 5000 rows of data to do the analysis and take a look at it.
complaints_test <- readRDS('complaints_sub.rds')
head(complaints_test,100) %>%
View()
glimpse(complaints_test)## Observations: 5,000
## Variables: 18
## $ Date.received <chr> "2019-08-23", "2019-10-17", "2018-03-2...
## $ Product <chr> "Credit reporting, credit repair servi...
## $ Sub.product <chr> "Credit reporting", "Credit reporting"...
## $ Issue <chr> "Incorrect information on your report"...
## $ Sub.issue <chr> "Information belongs to someone else",...
## $ Consumer.complaint.narrative <chr> "", "Equifax is reporting incorrectly ...
## $ Company.public.response <chr> "Company has responded to the consumer...
## $ Company <chr> "TRANSUNION INTERMEDIATE HOLDINGS, INC...
## $ State <chr> "GA", "CA", "FL", "CA", "IL", "NY", "T...
## $ ZIP.code <chr> "30349", "952XX", "347XX", "95835", "6...
## $ Tags <chr> "", "", "", "", "", "", "", "", "", ""...
## $ Consumer.consent.provided. <chr> "Consent not provided", "Consent provi...
## $ Submitted.via <chr> "Web", "Web", "Web", "Web", "Web", "We...
## $ Date.sent.to.company <chr> "2019-08-23", "2019-10-17", "2018-03-2...
## $ Company.response.to.consumer <chr> "Closed with explanation", "Closed wit...
## $ Timely.response. <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Ye...
## $ Consumer.disputed. <chr> "N/A", "N/A", "N/A", "No", "No", "No",...
## $ Complaint.ID <int> 3351802, 3409076, 2851469, 2373071, 11...5. Change date received to year-month-day
complaints_test <- complaints_test %>%
mutate(year = as.integer(substr(Date.received, start = 1, stop = 4))) %>%
mutate(month = as.integer(substr(Date.received, start = 6, stop = 7))) %>%
mutate(day = as.integer(substr(Date.received, start =9 , stop = 10)))
complaints_test$Date.received <- ymd(complaints_test$Date.received)
str(complaints_test)## 'data.frame': 5000 obs. of 21 variables:
## $ Date.received : Date, format: "2019-08-23" "2019-10-17" ...
## $ Product : chr "Credit reporting, credit repair services, or other personal consumer reports" "Credit reporting, credit repair services, or other personal consumer reports" "Credit reporting, credit repair services, or other personal consumer reports" "Mortgage" ...
## $ Sub.product : chr "Credit reporting" "Credit reporting" "Credit reporting" "Conventional fixed mortgage" ...
## $ Issue : chr "Incorrect information on your report" "Problem with a credit reporting company's investigation into an existing problem" "Incorrect information on your report" "Settlement process and costs" ...
## $ Sub.issue : chr "Information belongs to someone else" "Their investigation did not fix an error on your report" "Information belongs to someone else" "" ...
## $ Consumer.complaint.narrative: chr "" "Equifax is reporting incorrectly collections in the amount of {$48.00} ; {$42.00} and {$28.00} on the XXXX XXXX"| __truncated__ "CALLTHE COMPANY TOLD THEY THIS ACCOUNT DOSENT BELONG TO ME EVEN EMAIL AND DISPUTE THIS ACCOUNT" "" ...
## $ Company.public.response : chr "Company has responded to the consumer and the CFPB and chooses not to provide a public response" "" "Company has responded to the consumer and the CFPB and chooses not to provide a public response" "" ...
## $ Company : chr "TRANSUNION INTERMEDIATE HOLDINGS, INC." "EQUIFAX, INC." "Experian Information Solutions Inc." "Selene Finance LP" ...
## $ State : chr "GA" "CA" "FL" "CA" ...
## $ ZIP.code : chr "30349" "952XX" "347XX" "95835" ...
## $ Tags : chr "" "" "" "" ...
## $ Consumer.consent.provided. : chr "Consent not provided" "Consent provided" "Consent provided" "Consent not provided" ...
## $ Submitted.via : chr "Web" "Web" "Web" "Web" ...
## $ Date.sent.to.company : chr "2019-08-23" "2019-10-17" "2018-03-22" "2017-03-07" ...
## $ Company.response.to.consumer: chr "Closed with explanation" "Closed with explanation" "Closed with non-monetary relief" "Closed with explanation" ...
## $ Timely.response. : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Consumer.disputed. : chr "N/A" "N/A" "N/A" "No" ...
## $ Complaint.ID : int 3351802 3409076 2851469 2373071 1118688 1874286 3371286 3133247 1797570 1483556 ...
## $ year : int 2019 2019 2018 2017 2014 2016 2019 2019 2016 2015 ...
## $ month : int 8 10 3 3 11 4 9 1 2 7 ...
## $ day : int 23 17 22 7 16 11 11 25 22 23 ...6. Ploting of the number of complaints throughout the year
Ploting overall number of complaints throughout the year
p <- complaints_test %>%
mutate(date = as.POSIXct(paste(month , day , sep = "." ) , format = "%m.%d" )) %>%
group_by(date) %>%
summarise(number_of_complaints = n()) %>%
ggplot(aes(x = date, y = number_of_complaints))+ geom_point()+ylab("Number of complaints")+
theme_economist()+
scale_x_datetime(labels= date_format("%b-%d"),date_breaks = '1 month')+
geom_smooth(lwd=1, se=FALSE,color = 'red')+
geom_line(aes(x=date, y=SMA(number_of_complaints,10), color = 'red'))+
theme(legend.position="none")+
ggtitle("Overall number of complaints throughout a year")
ggplotly(p)In this graph, I plot a graph showing the number of complaints through out a year to see whether there are more complaints in certain months or day. We can see from the graph that there are a few outliars that are very low appears at the beginning of the month. On Jan-01, Mar-01, Jul-01 and Sep-01, the number of complaints drops significantly.
Ploting number of complaints of certain complany through out the year through out the year
simpleCap <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), substring(s, 2),
sep="", collapse=" ")
} # This line change the format of company names
complaints_pattern_company <- function(x){
p_ <- complaints_test %>%
filter(Company == x) %>%
mutate(date = as.POSIXct(paste(month , day , sep = "." ) , format = "%m.%d" )) %>%
group_by(date) %>%
summarise(number_of_complaints = n()) %>%
ggplot(aes(x = date, y = number_of_complaints))+ geom_point()+ylab("Number of complaints")+
theme_economist()+
scale_x_datetime(labels= date_format("%b-%d"),date_breaks = '1 month')+
geom_smooth(lwd=1, se=FALSE,color = 'red')+
geom_line(aes(x=date, y=SMA(number_of_complaints,10), color = 'red'))+
theme(legend.position="none")+
ggtitle(paste("Number of Complaints throughout a Year:",simpleCap(tolower(x)), sep = ' '))
ggplotly(p_)}
complaints_pattern_company('BANK OF AMERICA, NATIONAL ASSOCIATION')Ploting number of complaints of certain product through out the year through out the year
complaints_pattern_product <- function(x){
p_ <- complaints_test %>%
filter(Product == x) %>%
mutate(date = as.POSIXct(paste(month , day , sep = "." ) , format = "%m.%d" )) %>%
group_by(date) %>%
summarise(number_of_complaints = n()) %>%
ggplot(aes(x = date, y = number_of_complaints))+ geom_point()+ylab("Number of complaints")+
theme_economist()+
scale_x_datetime(labels= date_format("%b-%d"),date_breaks = '1 month')+
geom_smooth(lwd=1, se=FALSE,color = 'red')+
geom_line(aes(x=date, y=SMA(number_of_complaints,10), color = 'red'))+
theme(legend.position="none")+
ggtitle(paste("Number of Complaints throughout a Year:",simpleCap(tolower(x)), sep = ' '))
ggplotly(p_)}
complaints_pattern_product('Debt collection')Ploting companies with highest number of complaints
Top_10_comanies <- complaints_test %>%
group_by(Company) %>%
summarise(number_of_complaints = n())%>%
arrange(desc(number_of_complaints))%>%
head(10)%>%
select(Company) # This line select the companies with most complaints
p_1 <- complaints_test %>%
filter(Company %in% Top_10_comanies$Company) %>%
group_by(Company,Product)%>%
summarise(number_of_complaints = n())%>%
ungroup()%>%
mutate(Company = factor(Company, levels=Top_10_comanies$Company))%>%
ggplot(aes(fill=Product, y=number_of_complaints, x=Company)) +
geom_bar(position="stack", stat="identity")+ylab('Nunber of Complaints by Company')+coord_flip()+theme(legend.position="right")+guides(fill=guide_legend(nrow=15))
ggplotly(p_1,width = 1500, height = 500, type = 'bar')Ploting products with highest number of complaints
Top_10_product <- complaints_test %>%
group_by(Product) %>%
summarise(number_of_complaints = n())%>%
arrange(desc(number_of_complaints))%>%
head(10)%>%
select(Product) # This line select the companies with most complaints
p_2 <- complaints_test %>%
filter(Product %in% Top_10_product$Product) %>%
group_by(Product,Submitted.via)%>%
summarise(number_of_complaints = n())%>%
ungroup()%>%
mutate(Product = factor(Product, levels=Top_10_product$Product))%>%
ggplot(aes(fill=Submitted.via, y=number_of_complaints, x=Product)) +
geom_bar(position="stack", stat="identity")+ylab('Nunber of Complaints by Product')+coord_flip()+theme(legend.position="bottom")
ggplotly(p_2,width = 1200, height = 500, type = 'bar')7. Mapping the complaints in different states
The overall complaints number accross U.S.
census_api_key(Sys.getenv('CENSUS_API_KEY'))
state_pop <- get_acs(
geography = "state",
variables = "B01003_001",
year = 2018,
survey = "acs5"
)%>%
mutate(fips = fips(NAME))%>%
mutate(State = state.abb[match(NAME,state.name)])%>%
select(NAME,estimate,fips,State)%>%
mutate(state = NAME)%>%
mutate(state_population = estimate)%>%
select(-NAME,-estimate)
state_pop <- as.data.frame(state_pop)
state_complaint <- complaints_test %>%
group_by(State) %>%
summarise(number_of_complaints = n())%>%
ungroup()
state_complaint <- left_join(state_complaint,state_pop)
state_complaint <- state_complaint%>%
mutate(state_adjusted_complaints = number_of_complaints/state_population*100000)
state_complaint <- as.data.frame(state_complaint)%>%
filter(!is.na(state))
plot_usmap(data = state_complaint, values = "state_adjusted_complaints", color = "red")+scale_fill_continuous(
low = "white", high = "red", name = "Complaints by State (adjusted by population)", label = scales::comma) + theme(legend.position = "right")
complaints number accross U.S.
company_complaints_mapping <- function(x){
state_complaint <- complaints_test %>%
filter(Company == x) %>%
group_by(State) %>%
summarise(number_of_complaints = n())%>%
ungroup()
state_complaint <- left_join(state_complaint,state_pop)
state_complaint <- state_complaint%>%
mutate(state_adjusted_complaints = number_of_complaints/state_population*1000000)
state_complaint <- as.data.frame(state_complaint)%>%
filter(!is.na(state))
plot_usmap(data = state_complaint, values = "state_adjusted_complaints", color = "orange")+scale_fill_continuous(
low = "white", high = "orange", name = "Complaints by State (adjusted by population)", label = scales::comma) + theme(legend.position = "right")}
company_complaints_mapping('BANK OF AMERICA, NATIONAL ASSOCIATION')
8. Text mining and word cloud for complaints involving monetary relief or not.
set up corpus for narrative complaints
complaints_narrative_corp <- complaints_test %>%
select(Company.response.to.consumer,Consumer.complaint.narrative)%>%
filter(!is.na(Consumer.complaint.narrative))%>%
filter(Consumer.complaint.narrative != '')%>%
group_by(Company.response.to.consumer)%>%
summarise(narrative = paste0(Consumer.complaint.narrative,collapse = " "))%>%
ungroup()%>%
unnest_tokens(word, narrative) %>%
count(Company.response.to.consumer,word, sort = TRUE)data cleaning
complaints_narrative_corp <- complaints_narrative_corp %>%
filter(word %in% stopwords("english") == FALSE)%>%
filter(word %in% c("xxxx", "xxxxxxxx", "xx") == FALSE)%>%
mutate(word = tolower(word))%>%
filter(str_detect(word, "^[0-9]")==FALSE)%>%
filter(str_detect(word,'[[:punct:] ]+')==FALSE)%>%
filter(str_detect(word,' ')==FALSE)calculate number of words of each issue
total_words <- complaints_narrative_corp %>%
group_by(Company.response.to.consumer) %>%
summarize(total = sum(n))
complaints_narrative_corp <- left_join(complaints_narrative_corp, total_words)complaints_narrative_corp <- complaints_narrative_corp %>%
bind_tf_idf(word, Company.response.to.consumer, n)complaints_narrative_corp <- complaints_narrative_corp %>%
select(-total) %>%
arrange(desc(tf_idf))
str(complaints_narrative_corp)## Classes 'tbl_df', 'tbl' and 'data.frame': 13979 obs. of 6 variables:
## $ Company.response.to.consumer: chr "Untimely response" "Closed" "Closed" "Closed" ...
## $ word : chr "ameritech" "southwest" "nationstar" "ssn" ...
## $ n : int 8 5 6 6 24 2 2 111 3 3 ...
## $ tf : num 0.00977 0.00373 0.00447 0.00447 0.01788 ...
## $ idf : num 1.609 1.609 0.916 0.916 0.223 ...
## $ tf_idf : num 0.01572 0.006 0.0041 0.0041 0.00399 ...p_5 <- complaints_narrative_corp %>%
arrange(desc(tf_idf)) %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
group_by(Company.response.to.consumer) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(word, tf_idf, fill = Company.response.to.consumer)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~Company.response.to.consumer, ncol = 2, scales = "free") +
coord_flip()## Selecting by tf_idfggplotly(p_5,width = 1000, height = 1000, type = 'bar')word cloud with tf_idf
complaints_narrative_corp <- complaints_narrative_corp %>%
filter(Company.response.to.consumer == 'Closed with monetary relief')
wordcloud(words = complaints_narrative_corp$word, freq = complaints_narrative_corp$tf_idf,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
word cloud with frequency of words
wordcloud(words = complaints_narrative_corp$word, freq = complaints_narrative_corp$n,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))